1 citations · 2 across the 9 of their papers we have counts for
9 papers
LEGIT: Credentialing Protocol for Trustworthy AI Agent Marketplaces
Steve Drew, Jiayu Zhou
Agentic marketplaces are emerging where AI agents with varying capabilities autonomously complete specialized tasks for buyers. A major challenge of such marketplaces is that buyer…
FL-MAESTRO: Multi-Agent LLM Orchestration for Resource-Constrained Federated Learning
Jiajun Wu, Zirui Wang, Jiayu Zhou +2
In Federated Learning (FL), the communication topology is a runtime variable rather than a fixed design choice, since links and edge devices drop in and out during training. Each r…
Bitcoin Cross-Chain Bridge: A Taxonomy and Its Promise in Artificial Intelligence of Things
Guojun Tang, Carylyne Chan, Ning Nan +5
Bitcoin's limited scripting capabilities and lack of native interoperability mechanisms have constrained its integration into the broader blockchain ecosystem, especially decentral…
Dual-stage and Lightweight Patient Chart Summarization for Emergency Physicians
Jiajun Wu, Swaleh Zaidi, Braden Teitge +4
Electronic health records (EHRs) contain extensive unstructured clinical data that can overwhelm emergency physicians trying to identify critical information. We present a two-stag…
Lightweight and Robust Federated Data Valuation
Guojun Tang, Jiayu Zhou, Mohammad Mamun +1
Federated learning (FL) faces persistent robustness challenges due to non-IID data distributions and adversarial client behavior. A promising mitigation strategy is contribution ev…
Owen Sampling Accelerates Contribution Estimation in Federated Learning
Hossein KhademSohi, Hadi Hemmati, Jiayu Zhou +1
Federated Learning (FL) aggregates information from multiple clients to train a shared global model without exposing raw data. Accurately estimating each client's contribution is e…
FedGreen: Carbon-aware Federated Learning with Model Size Adaptation
Ali Abbasi, Fan Dong, Xin Wang +3
Federated learning (FL) provides a promising collaborative framework to build a model from distributed clients, and this work investigates the carbon emission of the FL process. Cl…
Federated Learning Model Aggregation in Heterogenous Aerial and Space Networks
Fan Dong, Ali Abbasi, Henry Leung +3
Federated learning offers a promising approach under the constraints of networking and data privacy constraints in aerial and space networks (ASNs), utilizing large-scale private e…
FedLE: Federated Learning Client Selection with Lifespan Extension for Edge IoT Networks
Jiajun Wu, Steve Drew, Jiayu Zhou
Federated learning (FL) is a distributed and privacy-preserving learning framework for predictive modeling with massive data generated at the edge by Internet of Things (IoT) devic…